Use of Real-Time Rig-Sensor Data To Improve Daily Drilling Reporting, Benchmarking, and Planning—A Case Study
- Wolfgang Mathis (TDE Thonhauser Data Engineering GmbH) | Gerhard Thonhauser (TDE Thonhauser Data Engineering GmbH) | Gerhard Wallnoefer (U. of Leoben) | Johannes Ettl (OMV UK Ltd.)
- Document ID
- Society of Petroleum Engineers
- SPE Drilling & Completion
- Publication Date
- September 2007
- Document Type
- Journal Paper
- 217 - 226
- 2007. Society of Petroleum Engineers
- 2.4.3 Sand/Solids Control, 1.10.1 Drill string components and drilling tools (tubulars, jars, subs, stabilisers, reamers, etc), 1.10 Drilling Equipment, 1.7.7 Cuttings Transport, 1.1 Well Planning, 1.6.1 Drilling Operation Management, 1.13 Drilling Automation, 4.3.4 Scale, 1.11.5 Drilling Hydraulics, 7.6.6 Artificial Intelligence, 1.12.6 Drilling Data Management and Standards, 1.7 Pressure Management, 4.1.2 Separation and Treating, 1.6 Drilling Operations, 1.6.5 Drilling Time Analysis
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State-of-the-art drilling-operations analysis is mostly dependent on existing daily-activity reporting. However, these activity reports are based on human observations and judgment. This fact implies a number of limitations such as the coarse level of detail and subjective coding systems. To overcome these problems, a rule-based system has been applied to analyze real-time surface-sensor data autonomously. The system evaluates the sensor data stream and acquires crucial process information as a basis for further analysis.
The scope of the system is the recognition of drilling operations, such as tripping, making connections, reaming, and washing, to extend and enhance standard reporting. In this way, a standardized and objective categorization of the drilling process can be achieved at a level of accuracy and detail yet to be reached.
Another benefit is the automated reporting feature. Through the recognition of the rigs, current state, the system is able to propose an impartial process description. This leads to a reduction of the time spent on reporting and leaves more time to focus on unexpected events and lessons learned.
Analysis of field data allowed the introduction of new key performance indicators (e.g., wellbore treatment time per depth interval) for benchmarking, which are determined automatically during the evaluation process. This type of benchmarking does not rely on company-specific activity coding systems. In this way, costly and time-consuming data management effort (e.g., to compare operated and nonoperated wells) is eliminated.
The new system was applied to wells drilled in the Vienna basin during the past year. The Vienna basin covers large parts of eastern Austria (lower Austria, Vienna, and Burgenland) and reaches into the territories of the Czech Republic in the north and the Slovak Republic in the east. It is approximately 200 km long and 55 km wide, striking roughly southwest to northeast from Gloggnitz (lower Austria) in the south to southwest to Napajedla (Czech Republic) in the north to northeast.
As a conclusion, it can be stated that the application of this system significantly improves the accuracy and resolution of the drilling-process description, reducing data-management efforts. The objective categorization of process information is a key enabler for benchmarking, specifically when identifying hidden lost time.
Most papers discussing drilling-activity performance analysis start with three simple questions (Bond et al. 1998; Iyoho et al. 2004; Adeleye et al. 2004): What is the current level of performance, what is the benchmark, and how can this gap be closed?
To improve performance, all of these questions have to be answered properly. As already discussed by Thonhauser (2004), the basis for most drilling-performance analysis work performed is the daily activity breakdown with all its drawbacks:
- Analysis based on subjective human observations
- Coarse level of detail
- Time-consuming data-entry and quality-control processes
In addition to these drawbacks, the current available personnel numbers and demographics in the petroleum industry leave very little room for extended analysis because of very tight time schedules. Experienced drilling engineers often do not have the required time to do proper analyses, while young, recently graduated engineers do not have the experience and knowledge to do so. Bond et al. (1998) stated: "The exercise of extracting removable time analysis was very time consuming (up to 2 man months for the eight wells reviewed) and required a high level of drilling/completion knowledge.??
Operating companies often try to reduce the cost of analysis by negotiating performance-driven contracts with drilling contractors. However, they often lack the means to subsequently evaluate and benchmark the service they get. If an ananysis is performed, the money spent on QC, performance analysis, and benchmarking is substantial and is often not worth the investment effort.
The time spent to drill a well is typically longer than what is technically possible. This is a result of down times. In Fig. 1, a refined graphical representation of Bond et al. (1998) is presented. The total lost time consists of two parts, the identified and the invisible lost time. Identified lost time consists of incidents that are reported in the drilling reports. An example would be "waiting for equipment.?? The invisible lost time is defined as time when the operations are not running at the technical limit. An example is the driller performing excessive reaming after each stand because of lack of experience.
In Bond et al. (1996), they estimate that approximately 40 to 60% of the total time must be accounted to the removable time. Comparing these numbers with other performance-improvement attempts, focusing on the identified lost time, where time reductions of 10 to 30 % are reported (Adeleye et al. 2004; Kadaster et al. 1992; Millheim et al. 1998), the potential invisible lost time accounts for approximately half of the removable time. This results in 20 to 30% of potential invisible lost time to be identified by the presented approach.
As mentioned, the identified lost time has already been addressed in several publications. State-of-the-art drilling-performance optimization uses the reported operations to work with, and is successful in doing so. However, it is only possible to identify only approximately half of the lost time, because the analysis is based on reported data with their coarse level of detail.
To go beyond these limitations, it is necessary to gather more details about the process being optimized.
|File Size||4 MB||Number of Pages||10|
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